{
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  "language_info": {
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    "name": "ipython",
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "source": [
    " TrainData = pd.read_table('Data/dataForTrainingLinear.txt',header=None,delim_whitespace=True)\n",
    " TestData = pd.read_table('Data/dataForTestingLinear.txt',header=None,delim_whitespace=True)\n",
    "#  print(type(data))\n",
    " print(TrainData.describe())\n",
    " print(TestData.describe())"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 44,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                0          1           2\ncount   50.000000  50.000000   50.000000\nmean   101.160000   5.006600  401.119400\nstd     25.438489   2.947746  190.309673\nmin     61.000000   0.300000   81.690000\n25%     79.500000   2.785000  232.660000\n50%     99.500000   4.425000  379.700000\n75%    119.500000   6.715000  522.477500\nmax    159.000000  12.400000  929.370000\n                0          1         2\ncount   10.000000  10.000000   10.0000\nmean    94.100000   3.096000  480.5620\nstd     18.537949   1.842132  135.0915\nmin     69.000000   0.780000  166.4600\n25%     79.250000   1.302500  472.4275\n50%     94.500000   3.510000  511.6550\n75%    106.250000   4.450000  536.9975\nmax    128.000000   5.540000  637.0700\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(50, 3)\n(10, 3)\n"
     ]
    }
   ],
   "source": [
    "trainds = TrainData.values\n",
    "testds = TestData.values\n",
    "print(trainds.shape)\n",
    "print(testds.shape)\n",
    "x_train = trainds[:,:2]\n",
    "y_train = trainds[:,2]\n",
    "x_test = testds[:,:2]\n",
    "y_test = testds[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建多元线性回归的模型\n",
    "def model(x,w,b):\n",
    "    return np.dot(x,w) + b\n",
    "def calculoss(err):\n",
    "    res = 0\n",
    "    for i in range(len(err)):\n",
    "        res += math.pow(err[i],2)\n",
    "    res = res / 2\n",
    "    return res\n",
    "# 构建损失函数\n",
    "def loss(x,y,w,b):\n",
    "    err = model(x,w,b) - y\n",
    "    # print(err)\n",
    "    loss = calculoss(err)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-40-538e5121dd64>:16: RuntimeWarning: overflow encountered in add\n  w_grad += (sampley[i]-y_train[i])*x_train[i]\n<ipython-input-40-538e5121dd64>:20: RuntimeWarning: invalid value encountered in subtract\n  w = w -alpha*w_grad\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-40-538e5121dd64>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0mtrainloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0mtestloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m     \u001b[0msampley\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m         \u001b[0mw_grad\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msampley\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-39-28ccc0968597>\u001b[0m in \u001b[0;36mmodel\u001b[1;34m(x, w, b)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 构建多元线性回归的模型\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcalculoss\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# 初始化需要训练的参数w和b\n",
    "w = np.random.rand(x_train.shape[1])\n",
    "b = np.random.rand(1)\n",
    "trainloss_arr = [] # 存放训练集的误差\n",
    "testloss_arr = [] # 存放测试集的误差\n",
    "alpha = 0.000015 # 设置的学习率\n",
    "epochnum = 1500000\n",
    "for epoch in range(epochnum):\n",
    "    # gradient descent\n",
    "    w_grad = 0\n",
    "    b_grad =0\n",
    "    trainloss = 0\n",
    "    testloss = 0\n",
    "    sampley = model(x_train,w,b)\n",
    "    for i in range(len(x_train)):\n",
    "        w_grad += (sampley[i]-y_train[i])*x_train[i]\n",
    "        b_grad += (sampley[i]-y_train[i])\n",
    "    w_grad = w_grad / len(x_train)\n",
    "    b_grad = b_grad / len(x_train)\n",
    "    w = w -alpha*w_grad\n",
    "    b = b -alpha*b_grad\n",
    "    if (epoch+1) % 100000 == 0:\n",
    "        trainloss = loss(x_train,y_train,w,b)\n",
    "        trainloss_arr.append(trainloss)\n",
    "        testloss = loss(x_test,y_test,w,b)\n",
    "        testloss_arr.append(testloss)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "print(trainloss_arr)\n",
    "print(testloss_arr)\n",
    "plt.title('The loss of gradient descent')\n",
    "plt.xlabel('Epoch number')\n",
    "plt.ylabel('loss result')\n",
    "plt.plot(trainloss_arr,marker = 'o',label = 'training loss')\n",
    "plt.plot(testloss_arr,marker = '<',label = 'testing loss')\n",
    "plt.legend(loc=0,ncol=2)\n",
    "print(w)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 初始化需要训练的参数w和b\n",
    "w = np.random.rand(x_train.shape[1])\n",
    "b = np.random.rand(1)\n",
    "trainloss_arr = [] # 存放训练集的误差\n",
    "testloss_arr = [] # 存放测试集的误差\n",
    "alpha = 0.00015 # 设置的学习率\n",
    "epochnum = 1500000\n",
    "for epoch in range(epochnum):\n",
    "    #随机梯度下降算法\n",
    "    w_grad = 0\n",
    "    b_grad =0\n",
    "    trainloss = 0\n",
    "    testloss = 0\n",
    "    sampley = model(x_train,w,b)\n",
    "    index = np.random.randint(len(x_train)-1)\n",
    "    w_grad = (sampley[index]-y_train[index])*x_train[index]\n",
    "    b_grad = (sampley[index]-y_train[index])\n",
    "    w = w -alpha*w_grad\n",
    "    b = b -alpha*b_grad\n",
    "    if (epoch+1) % 100000 == 0:\n",
    "        trainloss = loss(x_train,y_train,w,b)\n",
    "        trainloss_arr.append(trainloss)\n",
    "        testloss = loss(x_test,y_test,w,b)\n",
    "        testloss_arr.append(testloss) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[1840.5868063010676, 22901.981461328935, 1534.4678397885036, 2000.8388701767633, 166.97966761849648, 1467.719292431937, 464.95925205035326, 189.1915089399595, 318.7694774422336, 579.2116209069592, 185.27898650765002, 120.67666366979364, 177.33772698226682, 312.1295228461288, 96.08162875860583]\n[488.6892722084398, 1613.546553631271, 1602.1212113038769, 87.50025149371638, 526.2803110834319, 1702.0115497268418, 317.10410503185255, 480.63484281048864, 1033.051796307389, 1247.5494171829926, 458.53940776113836, 740.1641763472579, 494.2811762740247, 999.5063550641231, 662.2348981563262]\n[  6.7576202 -72.3934903]\n[79.86200171]\n[631.34485766 714.803989   558.33578171 698.64206838 647.50677828\n 799.48805515 580.95349951 347.09248802 253.30309647 498.83909815\n 198.53630657 307.7003822  130.53172275 191.96901071 178.9692564\n 218.1749237  461.26105578 566.74877627 297.75959202 436.05708021\n 366.68780055 386.18665664 418.55042961 426.67715633 339.271995\n 456.06520151 324.37156803 143.88931186 369.54373317 521.71974505\n 189.88034878 212.56626066 477.01691339 246.18660367 538.70546107\n 496.67549638 215.59358956 252.81379712 281.12004142 167.73196446\n 258.93172537 371.58416687 419.76369504 221.26552306 185.07551528\n  74.96110332 631.71098895 593.22928773 916.19802533 429.3500233 ]\n[637.07 494.08 502.26 166.46 521.05 539.17 530.48 465.21 347.3  602.54]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "print(trainloss_arr)\n",
    "print(testloss_arr)\n",
    "plt.title('The loss of stochastic gradient descent')\n",
    "plt.xlabel('Epoch number')\n",
    "plt.ylabel('loss result')\n",
    "plt.plot(trainloss_arr,marker = 'o',label = 'training loss')\n",
    "plt.plot(testloss_arr,marker = '<',label = 'testing loss')\n",
    "plt.legend(loc=0,ncol=2)\n",
    "print(w)\n",
    "print(b)\n",
    "print(sampley)\n",
    "print(y_test) "
   ]
  }
 ]
}